1. Technological Field
This technical disclosure pertains generally to image segmentation, and more particularly to utilizing a Bayes risk assessment in the determination of which pixels are assigned to a foreground and a background.
2. Background Discussion
Image segmentation is a process of partitioning an image into regions under certain rules. The simplest case would be to separate the foreground object, such as humans, from the background, or conversely the background from the foreground. Image segmentation can be utilized as a basis for many image processing operations, including deleting or moving image objects, generating 3D effects, stroboscopic imaging, autofocusing, and so forth.
In many images it can be challenging to discern the foreground elements from the background. This problem arises as there are portions of an image which could be selected to be either with the foreground or in the background. That is to say the metrics used to decide whether a pixel is to be in the foreground/background are not definitive—and probabilities in some cases are as low as 50%, making any decision questionable (“iffy”). The result of improper choices is that one or more artifacts from the background remain in, or attached to, the foreground object or conversely that elements of the foreground remain in the background. Unfortunately, the quality of a given segmentation process is significantly determined on the basis of this discernment.
Accordingly, a need exists for a method of improving these choices when assigning image pixel elements to the foreground or to the background. The present disclosure presents such a solution while overcoming shortcomings of previous segmentation selection mechanisms.